Approach for Document Detection by Contours and Contrasts

• URL: http://arxiv.org/abs/2008.02615v2
• Date: Mon, 19 Oct 2020 14:03:44 GMT
• Title: Approach for Document Detection by Contours and Contrasts
• Authors: Daniil V. Tropin, Sergey A. Ilyuhin, Dmitry P. Nikolaev and Vladimir V. Arlazarov
• Abstract summary: This paper considers arbitrary document detection performed on a mobile device. We propose a modification of the contour-based method, in which the competing contour location hypotheses are ranked according to the contrast between the areas inside and outside the border. The proposed method provides unmatched state-of-the-art performance on the open MIDV-500 dataset, and it demonstrates results comparable with state-of-the-art performance on the SmartDoc dataset.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper considers arbitrary document detection performed on a mobile device. The classical contour-based approach often fails in cases featuring occlusion, complex background, or blur. The region-based approach, which relies on the contrast between object and background, does not have application limitations, however, its known implementations are highly resource-consuming. We propose a modification of the contour-based method, in which the competing contour location hypotheses are ranked according to the contrast between the areas inside and outside the border. In the experiments, such modification allows for the decrease of alternatives ordering errors by 40% and the decrease of the overall detection errors by 10%. The proposed method provides unmatched state-of-the-art performance on the open MIDV-500 dataset, and it demonstrates results comparable with state-of-the-art performance on the SmartDoc dataset.

Related papers

• COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection [56.7599217711363]
  Face forgery recognition methods can only process one face at a time. Most face forgery recognition methods can only process one face at a time. We propose COMICS, an end-to-end framework for multi-face forgery detection.
  arXiv  Detail & Related papers  (2023-08-03T03:37:13Z)
• Deep Metric Learning with Soft Orthogonal Proxies [1.823505080809275]
  We propose a novel approach that introduces Soft Orthogonality (SO) constraint on proxies. Our approach leverages Data-Efficient Image Transformer (DeiT) as an encoder to extract contextual features from images along with a DML objective. Our evaluations demonstrate the superiority of our proposed approach over state-of-the-art methods by a significant margin.
  arXiv  Detail & Related papers  (2023-06-22T17:22:15Z)
• UDoc-GAN: Unpaired Document Illumination Correction with Background Light Prior [128.19212716007794]
  UDoc-GAN is first framework to address the problem of document illumination correction under the unpaired setting. We first predict the ambient light features of the document. Then, according to the characteristics of different level of ambient lights, we re-formulate the cycle consistency constraint. Compared with the state-of-the-art approaches, our method demonstrates promising performance in terms of character error rate (CER) and edit distance (ED)
  arXiv  Detail & Related papers  (2022-10-15T07:19:23Z)
• Fake It Till You Make It: Near-Distribution Novelty Detection by Score-Based Generative Models [54.182955830194445]
  existing models either fail or face a dramatic drop under the so-called near-distribution" setting. We propose to exploit a score-based generative model to produce synthetic near-distribution anomalous data. Our method improves the near-distribution novelty detection by 6% and passes the state-of-the-art by 1% to 5% across nine novelty detection benchmarks.
  arXiv  Detail & Related papers  (2022-05-28T02:02:53Z)
• Confidence Score for Unsupervised Foreground Background Separation of Document Images [5.279475826661642]
  We propose a novel approach for computing confidence scores of the classification in such algorithms. The computational complexity of the proposed approach is the same as the underlying binarization algorithm.
  arXiv  Detail & Related papers  (2022-04-03T18:22:11Z)
• Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection [97.93062818228015]
  We propose to integrate the reconstruction-based functionality into a novel self-supervised predictive architectural building block. Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field. We demonstrate the generality of our block by integrating it into several state-of-the-art frameworks for anomaly detection on image and video.
  arXiv  Detail & Related papers  (2021-11-17T13:30:31Z)
• Advanced Hough-based method for on-device document localization [0.0]
  In this work, we consider document location in an image without prior knowledge of the document content or its internal structure. We propose an advanced Hough-based method which accounts for the geometric invariants of the central projection model. When evaluated on a more challenging MIDV-500 dataset, the proposed algorithm guaranteed the best precision compared to published methods.
  arXiv  Detail & Related papers  (2021-06-18T08:17:45Z)
• Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction [61.17654438176999]
  We propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design. Our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.
  arXiv  Detail & Related papers  (2020-11-05T11:34:12Z)
• UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders [81.5490760424213]
  We propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Inspired by the saliency data labeling process, we propose probabilistic RGB-D saliency detection network.
  arXiv  Detail & Related papers  (2020-04-13T04:12:59Z)
• Unsupervised Pixel-level Road Defect Detection via Adversarial Image-to-Frequency Transform [8.644679871804872]
  We propose an unsupervised approach to detecting road defects, using Adversarial Image-to-Frequency Transform (AIFT) AIFT adopts the unsupervised manner and adversarial learning in deriving the defect detection model, so AIFT does not need annotations for road pavement defects.
  arXiv  Detail & Related papers  (2020-01-30T04:50:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.